86 lines
3.3 KiB
Markdown
86 lines
3.3 KiB
Markdown
# Findings & Decisions
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WHAT: Knowledge base for your analytics session. Stores data sources, hypotheses, and results.
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WHY: Context windows are limited. This file is your "external memory" for analytical work.
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WHEN: Update after ANY discovery, especially after running queries or viewing charts.
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## Data Sources
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WHAT: Every data source you connected to, with schema details and quality notes.
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WHY: Knowing where your data came from and its limitations is critical for reproducibility.
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EXAMPLE:
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| user_events | PostgreSQL prod replica | 2.3M rows | user_id, event_type, ts | 0.2% null user_id |
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| revenue.csv | Finance team export | 45K rows | account_id, mrr, churn_date | Complete, no nulls |
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| Source | Location | Size | Key Fields | Quality Notes |
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|--------|----------|------|------------|---------------|
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## Hypothesis Log
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WHAT: Each hypothesis you tested, the method used, and the result.
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WHY: Structured tracking prevents p-hacking and makes your reasoning auditable.
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EXAMPLE:
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| H1: Churn > 50% for low-activity users | Chi-squared test | Confirmed (p=0.003) | High |
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| H2: Feature X correlates with retention | Pearson correlation | Rejected (r=0.08) | High |
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| Hypothesis | Test Method | Result | Confidence |
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|------------|-------------|--------|------------|
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## Query Results
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WHAT: Key queries you ran and what they revealed.
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WHY: Queries are ephemeral - if you don't write down the results, they're lost on context reset.
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WHEN: After EVERY significant query. Don't wait.
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EXAMPLE:
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### Churn rate by activity segment
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Query: SELECT activity_bucket, COUNT(*), AVG(churned) FROM user_segments GROUP BY 1
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Result: Low activity: 62% churn, Medium: 28%, High: 8%
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Interpretation: Strong inverse relationship between activity and churn
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<!-- Record query, result summary, and interpretation for each significant query -->
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## Statistical Findings
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WHAT: Formal statistical test results with all relevant metrics.
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WHY: Recording p-values, effect sizes, and confidence intervals makes results reproducible.
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EXAMPLE:
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| Chi-squared (churn ~ activity) | p=0.003 | Cramer's V=0.31 | Reject null: activity segments differ significantly in churn |
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| Pearson (feature_x ~ retention) | p=0.42 | r=0.08 | Fail to reject: no meaningful correlation |
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| Test | p-value | Effect Size | Conclusion |
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|------|---------|-------------|------------|
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## Technical Decisions
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WHAT: Analytical method choices with reasoning.
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EXAMPLE:
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| Use log transform on revenue | Right-skewed distribution, normalizes for parametric tests |
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| Decision | Rationale |
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|----------|-----------|
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## Issues Encountered
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| Issue | Resolution |
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|-------|------------|
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## Resources
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<!-- URLs, file paths, documentation links -->
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## Visual/Browser Findings
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CRITICAL: Update after viewing charts, dashboards, or browser results.
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Multimodal content doesn't persist in context - capture as text immediately.
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---
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*Update this file after every 2 view/browser/search operations*
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*This prevents visual information from being lost*
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